def __init__(self, nx=10, ny=10, xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0):
self.xmin, self.xmax, self.ymin, self.ymax = xmin, xmax, ymin, ymax
self.dx = float(xmax - xmin) / (nx - 1)
self.dy = float(ymax - ymin) / (ny - 1)
self.u = numpy.zeros((nx, ny), 'd')
# used to compute the change in solution in some of the methods.
self.old_u = self.u.copy()
def __init__(self, nx=10, ny=10, xmin=0.0, xmax=1.0, ymin=0.0, ymax=1.0):
self.xmin, self.xmax, self.ymin, self.ymax = xmin, xmax, ymin, ymax
self.dx = float(xmax - xmin) / (nx - 1)
self.dy = float(ymax - ymin) / (ny - 1)
self.u = numpy.zeros((nx, ny), 'd')
# used to compute the change in solution in some of the methods.
self.old_u = self.u.copy()
def jacobi(A, B, tol=0.005, forcedIter=0):
'''itteratively solving for matrix A with solution vector B
tol = tolerance for dh/h
init_val = array of initial values to use in the solver
'''
h = np.zeros(np.shape(B), float)
dmax = 1.0
n = 0
tmp0 = np.empty(np.shape(A), float)
tmp1 = np.empty(np.shape(B), float)
AD = np.diagonal(A)
#np.timer_reset()
#np.evalflush()
t1 = time.time()
while (forcedIter and forcedIter > n) or \
(forcedIter == 0 and dmax > tol):
n += 1
np.multiply(A, h, tmp0)
np.add.reduce(tmp0, 1, out=tmp1)
tmp2 = AD
np.subtract(B, tmp1, tmp1)
np.divide(tmp1, tmp2, tmp1)
hnew = h + tmp1
np.subtract(hnew, h, tmp2)
np.divide(tmp2, h, tmp1)
np.absolute(tmp1, tmp1)
dmax = np.maximum.reduce(tmp1)
h = hnew
print dmax
#np.evalflush()
t1 = time.time() - t1
print 'Iter: ', n, ' size: ', np.shape(B), ' time: ', t1,
print "(Dist) notes: %s" % sys.argv[4]
#if A.dist():
# print "(Dist) notes: %s"%sys.argv[4]
#else:
# print "(Non-Dist) notes: %s"%sys.argv[4]
return h
def jacobi(A, B, tol=0.005, forcedIter=0):
"""itteratively solving for matrix A with solution vector B
tol = tolerance for dh/h
init_val = array of initial values to use in the solver
"""
h = np.zeros(np.shape(B), float)
dmax = 1.0
n = 0
tmp0 = np.empty(np.shape(A), float)
tmp1 = np.empty(np.shape(B), float)
AD = np.diagonal(A)
# np.timer_reset()
# np.evalflush()
t1 = time.time()
while (forcedIter and forcedIter > n) or (forcedIter == 0 and dmax > tol):
n += 1
np.multiply(A, h, tmp0)
np.add.reduce(tmp0, 1, out=tmp1)
tmp2 = AD
np.subtract(B, tmp1, tmp1)
np.divide(tmp1, tmp2, tmp1)
hnew = h + tmp1
np.subtract(hnew, h, tmp2)
np.divide(tmp2, h, tmp1)
np.absolute(tmp1, tmp1)
dmax = np.maximum.reduce(tmp1)
h = hnew
print dmax
# np.evalflush()
t1 = time.time() - t1
print "Iter: ", n, " size: ", np.shape(B), " time: ", t1,
print "(Dist) notes: %s" % sys.argv[4]
# if A.dist():
# print "(Dist) notes: %s"%sys.argv[4]
# else:
# print "(Non-Dist) notes: %s"%sys.argv[4]
return h
#np.ufunc_random(PyT,PyT)
PyT = np.random.random_sample((1,n))
#PzT= np.empty((1,n))
#np.ufunc_random(PzT,PzT)
PzT = np.random.random_sample((1,n))
#Vx = np.empty((n,1))
#np.ufunc_random(Vx,Vx)
Vx = np.random.random_sample((n,1))
#Vy = np.empty((n,1))
#np.ufunc_random(Vy,Vy)
Vy = np.random.random_sample((n,1))
#Vz = np.empty((n,1))
#np.ufunc_random(Vz,Vz)
Vz = np.random.random_sample((n,1))
OnesCol = np.zeros((n,1), dtype=float)+1.0
OnesRow = np.zeros((1,n), dtype=float)+1.0
#Identity= array(diag([1]*n), dtype=double)
#np.timer_reset()
#np.evalflush()
stime = time.time()
for i in xrange(k):
#distance between all pairs of objects
Fx = np.dot(OnesCol, PxT) - np.dot(Px, OnesRow)
Fy = np.dot(OnesCol, PyT) - np.dot(Py, OnesRow)
Fz = np.dot(OnesCol, PzT) - np.dot(Pz, OnesRow)
Dsq = Fx * Fx
Dsq += Fy * Fy
Dsq += Fz * Fz
